Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search
arXiv:1409.8053 · doi:10.1016/j.dss.2005.02.005
Abstract
This paper describes a novel approach to medical diagnosis based on the SP theory of computing and cognition. The main attractions of this approach are: a format for representing diseases that is simple and intuitive; an ability to cope with errors and uncertainties in diagnostic information; the simplicity of storing statistical information as frequencies of occurrence of diseases; a method for evaluating alternative diagnostic hypotheses that yields true probabilities; and a framework that should facilitate unsupervised learning of medical knowledge and the integration of medical diagnosis with other AI applications.
References in corpus (4)
- Mathematics and Logic as Information Compression by Multiple Alignment, Unification and Search
- Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search
- Unsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search
- Information Compression by Multiple Alignment, Unification and Search as a Unifying Principle in Computing and Cognition
Cited by in corpus (5)
- Autonomous robots and the SP theory of intelligence
- Solutions to problems with deep learning
- The SP Theory of Intelligence as a Foundation for the Development of a General, Human-Level Thinking Machine
- Computing as compression: the SP theory of intelligence
- Simplification and integration in computing and cognition: the SP theory and the multiple alignment concept